"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import re
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils import model_zoo

__all__ = [
    'densenet121', 'densenet169', 'densenet201', 'densenet161',
    'densenet121_fc512'
]

model_urls = {
    'densenet121':
    'https://download.pytorch.org/models/densenet121-a639ec97.pth',
    'densenet169':
    'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
    'densenet201':
    'https://download.pytorch.org/models/densenet201-c1103571.pth',
    'densenet161':
    'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}


class _DenseLayer(nn.Sequential):

    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU(inplace=True)),
        self.add_module(
            'conv1',
            nn.Conv2d(
                num_input_features,
                bn_size * growth_rate,
                kernel_size=1,
                stride=1,
                bias=False
            )
        ),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU(inplace=True)),
        self.add_module(
            'conv2',
            nn.Conv2d(
                bn_size * growth_rate,
                growth_rate,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False
            )
        ),
        self.drop_rate = drop_rate

    def forward(self, x):
        new_features = super(_DenseLayer, self).forward(x)
        if self.drop_rate > 0:
            new_features = F.dropout(
                new_features, p=self.drop_rate, training=self.training
            )
        return torch.cat([x, new_features], 1)


class _DenseBlock(nn.Sequential):

    def __init__(
        self, num_layers, num_input_features, bn_size, growth_rate, drop_rate
    ):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i*growth_rate, growth_rate, bn_size,
                drop_rate
            )
            self.add_module('denselayer%d' % (i+1), layer)


class _Transition(nn.Sequential):

    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module(
            'conv',
            nn.Conv2d(
                num_input_features,
                num_output_features,
                kernel_size=1,
                stride=1,
                bias=False
            )
        )
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    """Densely connected network.
    
    Reference:
        Huang et al. Densely Connected Convolutional Networks. CVPR 2017.

    Public keys:
        - ``densenet121``: DenseNet121.
        - ``densenet169``: DenseNet169.
        - ``densenet201``: DenseNet201.
        - ``densenet161``: DenseNet161.
        - ``densenet121_fc512``: DenseNet121 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        num_init_features=64,
        bn_size=4,
        drop_rate=0,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):

        super(DenseNet, self).__init__()
        self.loss = loss

        # First convolution
        self.features = nn.Sequential(
            OrderedDict(
                [
                    (
                        'conv0',
                        nn.Conv2d(
                            3,
                            num_init_features,
                            kernel_size=7,
                            stride=2,
                            padding=3,
                            bias=False
                        )
                    ),
                    ('norm0', nn.BatchNorm2d(num_init_features)),
                    ('relu0', nn.ReLU(inplace=True)),
                    (
                        'pool0',
                        nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
                    ),
                ]
            )
        )

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate
            )
            self.features.add_module('denseblock%d' % (i+1), block)
            num_features = num_features + num_layers*growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(
                    num_input_features=num_features,
                    num_output_features=num_features // 2
                )
                self.features.add_module('transition%d' % (i+1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.feature_dim = num_features
        self.fc = self._construct_fc_layer(fc_dims, num_features, dropout_p)

        # Linear layer
        self.classifier = nn.Linear(self.feature_dim, num_classes)

        self._init_params()

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer.

        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        f = self.features(x)
        f = F.relu(f, inplace=True)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if self.fc is not None:
            v = self.fc(v)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError('Unsupported loss: {}'.format(self.loss))


def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)

    # '.'s are no longer allowed in module names, but pervious _DenseLayer
    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    # They are also in the checkpoints in model_urls. This pattern is used
    # to find such keys.
    pattern = re.compile(
        r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$'
    )
    for key in list(pretrain_dict.keys()):
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            pretrain_dict[new_key] = pretrain_dict[key]
            del pretrain_dict[key]

    model_dict = model.state_dict()
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


"""
Dense network configurations:
--
densenet121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)
densenet169: num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32)
densenet201: num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32)
densenet161: num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24)
"""


def densenet121(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = DenseNet(
        num_classes=num_classes,
        loss=loss,
        num_init_features=64,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['densenet121'])
    return model


def densenet169(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = DenseNet(
        num_classes=num_classes,
        loss=loss,
        num_init_features=64,
        growth_rate=32,
        block_config=(6, 12, 32, 32),
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['densenet169'])
    return model


def densenet201(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = DenseNet(
        num_classes=num_classes,
        loss=loss,
        num_init_features=64,
        growth_rate=32,
        block_config=(6, 12, 48, 32),
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['densenet201'])
    return model


def densenet161(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = DenseNet(
        num_classes=num_classes,
        loss=loss,
        num_init_features=96,
        growth_rate=48,
        block_config=(6, 12, 36, 24),
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['densenet161'])
    return model


def densenet121_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = DenseNet(
        num_classes=num_classes,
        loss=loss,
        num_init_features=64,
        growth_rate=32,
        block_config=(6, 12, 24, 16),
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['densenet121'])
    return model
